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Jumping Around Can Help Kids Learn Math

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iStock

Sitting quietly at a desk may be the preferred behavior for elementary-school students, but that doesn’t mean it’s the best way for them to learn. Researchers in Denmark have found that integrating whole-body movement into math lessons can significantly boost kids’ test scores. They published their research in the journal Frontiers of Human Neuroscience

We all know that being active is good for our whole bodies. Recent studies have shown that those benefits reach all the way into the brain for both adults and kids. Intense exertion—the kind that gets your heart rate up—may improve alertness, and is linked to improved motor skills, sharper thinking, and better grades.

So we know that exercise can boost our brainpower. But can it help us learn? To find out, health scientists at the University of Copenhagen created a movement-centric, six-week math curriculum for elementary students. They recruited 165 pupils, all around the age of 7, and divided them into three groups. Some classes were given math lessons three times a week that required them to use their whole bodies (gross motor skills). They jumped, skipped, and crawled around the classroom, all while solving math problems.

Classes in the second group were sedentary but added fine motor skill activities to their lessons—that is, the students were asked to use LEGO bricks to help them solve math problems. 

Kids in the third group, the control group, had their normal math instruction.

All the students were given standardized math tests before, immediately after, and eight weeks after the experiment. (Standardized test scores are not necessarily the best way to measure kids’ understanding, but they do provide a quantitative baseline by which to gauge improvement over the course of an experiment.) 

Over the course of the six-week study, all three groups’ scores improved, but there was a clear winner. Kids in the crawling-skipping-jumping group saw the biggest boost in their scores, improving twice as much as students in the LEGO classes. The upswing in the gross motor skills group’s test scores was modest—about 7.6 percent—but still significant.

“We need to keep this in mind when developing new forms of instruction,” lead author Jacob Wienecke said in a statement.

Unfortunately, the score bump was not universal. Kids who struggled with math at the beginning of the study were still struggling afterward.

“Individual understanding must be taken into account,” Wienecke said. “Otherwise, we risk an unfortunate combined outcome in which those who are already proficient advance, and those who have not yet mastered concepts cannot keep up." 

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iStock // Ekaterina Minaeva
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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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